Research map / Learning theory and algorithms / Prediction and decision losses
k*-Nearest Neighbors: From Global to Local
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use k*-NN instead of Nadaraya-Watson with a Gaussian kernel | k*-NN · QSAR | absolute error: 0.2303 -> 0.2105 | Helped |
| Use k*-NN instead of Nadaraya-Watson with a Gaussian kernel | k*-NN · Diabetes | absolute error: 0.3675 -> 0.3666 | Helped |
| Use k*-NN instead of Nadaraya-Watson with a Gaussian kernel | k*-NN · PopFailures | absolute error: 0.1155 -> 0.1218 | Hurt |
| Use k*-NN instead of Nadaraya-Watson with a Gaussian kernel | k*-NN · Sonar | absolute error: 0.1711 -> 0.1636 | Helped |
| Use k*-NN instead of Nadaraya-Watson with a Gaussian kernel | k*-NN · Ionosphere | absolute error: 0.1191 -> 0.1113 | Helped |
| Use k*-NN instead of Nadaraya-Watson with a Gaussian kernel | k*-NN · Fertility | absolute error: 0.1884 -> 0.1760 | Helped |
| Use k*-NN instead of Nadaraya-Watson with a Gaussian kernel | k*-NN · Slump | absolute error: 2.9154 -> 2.8057 | Helped |
| Use k*-NN instead of Nadaraya-Watson with a Gaussian kernel | k*-NN · Yacht | absolute error: 5.2577 -> 5.0418 | Helped |
Closest papers by meaning
- Distributionally robust weighted k-nearest neighbors (NeurIPS 2022 · similarity 0.90)
- Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate (NeurIPS 2020 · similarity 0.89)
- DNNR: Differential Nearest Neighbors Regression (ICML 2022 · similarity 0.89)
- An adaptive nearest neighbor rule for classification (NeurIPS 2019 · similarity 0.89)
- Weighted distance nearest neighbor condensing (ICML 2024 · similarity 0.87)
- Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later (ICLR 2025 · similarity 0.84)
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